1. Field of the Invention
The present invention relates to the field of computer imaging, and, more particularly, to local surface smoothing in a defined Volume of Interest (“VOI”) cropped from a three-dimensional volume data.
2. Description of the Related Art
Modem advances in medical imaging equipment have provided greater efficiency and higher capability for screening, diagnosis and surgery of various kinds of diseases. Three-dimensional (“3D”) imaging modalities, such as multi-slice computer tomography (“CT”) scanners, produce a large amount of digital data that is generally difficult and tedious for a human (e.g., a physician) to interpret without additional aid. Computer-aided diagnosis (“CAD”) systems play a critical role in aiding the human, especially in the visualization, segmentation, detection, registration, and reporting of medical pathologies.
One of the more critical CAD tasks includes the screening and early detection of various types of cancer from a volume data (e.g., a CT volume data). For instance, lung cancer is the leading cause of deaths among all cancers in the United States and around the world. A patient diagnosed with lung cancer has an average five-year survival rate of only 14%. On the other hand, if lung cancer is diagnosed in stage I, the patient's expected five-year survival rate dramatically increases to between 60 and 70 percent. Other cancers, such as colon cancer, have also shown a decrease in mortality rates resulting from the early detection and removal of cancerous tumors. Unfortunately, existing methods generally do not detect characteristic symptoms of various cancers until the advanced stages of the disease. Therefore, a primary goal in advancing preventive cancer screening is to provide for earlier detection of the characteristic symptoms.
Among various CAD functionalities for cancer screening, automatic segmentation of suspicious regions of interest is typically a crucial step for further analysis. This includes quantification, feature measurements, classification, and nodule growth rate determination. The possible pathological regions are often connected with the surrounding anatomical structures. Segmentation of such regions from the connected anatomies is a challenging problem, primarily because there is little different between the intensities of the pathology and the anatomy. Therefore, shape analysis is often performed to separate the pathologies instead of simple threshold based techniques.
Pathologies are typically spherical or hemispherical in geometric shape. In many cases, these sphere-like pathologies are attached to linear or piece-wise linear surfaces. For example, in lung cancer screening, one of the major goals is to detect, segment and monitor the growth of small tumors (i.e., nodules) within the lung regions. The lungs contain complex structures of branching vessels and airways. Lung nodules may be found throughout the lungs, including attached to pleura or vessels. The segmentation of a nodule from the pleura is a challenging task. The nodule attached to pleura may be the shape of a hemisphere bump on a relatively smooth and linear chest wall surface. For another example, in colon cancer screening, small tumors attached to the inner colon surfaces (i.e., polyps) are the major potential pathologies to detect, segment, and monitor. Like the nodule, a polyp may also be the shape of a hemisphere bump sitting on the relatively smooth and piecewise linear cylindrical inner surface of the colon.
The segmentation of potential pathologies, such as pleura-attached nodules and the colon-attached polyps, from the surrounding anatomical structures, can be viewed as a local surface-smoothing problem. When a user clicks on a nodule or polyp, a volume of interest (“VOI”) can be defined around the user's click point. The CAD system may fix the size of the VOI to contain the largest nodule. The inner surfaces of the pleura and the colon are mostly smooth and piecewise linear, with the nodule or the polyp being the only abrupt discontinuity of smoothness and piecewise linearity. Surface smoothing removes the bump on the surface, and the difference between the smoothed VOI and the original VOI will be the segmented nodule or the polyp.
An exemplary nodule segmentation method will now be described. In a user-defined click point, the method performs a region grow from the center and obtains the foreground voxels. The foreground structure comprises a nodule candidate, and, optionally, attached vessels and a portion of the pleura. If the method determines that there is a pleura (or chest wall) inside the VOI, a chest wall exclusion procedure is activated. The chest wall is smoothed using a rolling ball-based method. The remaining foreground structure is named a structure of interest (“SOI”). Shape analysis is performed to obtain the core of the SOI and the center of the core. A 3D spherical template is applied to the core. The method iteratively expands the template, and computes a cross correlation curve. The template is optimized based on the analysis of the cross correlation curve. The resulting segment is the portion of the SOI that overlaps with the optimal spherical template.
A key step for the above segmentation of pleura-attached nodules is the exclusion of the chest wall section in the binary VOI. The above exemplary method as well as other known methods are 2D-based. The VOI is considered to be a set of 2D axial slice images. On each slice, the binary image of foreground regions is traced and the contour is analyzed using curvature information. The rolling ball method assumes a piecewise linear contour with abrupt bumps to be removed. The rolling ball is placed at each high-curvature point of the contour, and, if there is more than one intersection with the contour, the contour section between the intersection points are replaced by a line.
The exemplary nodule segmentation method described above is, among other things, imprecise, error-prone in noisy situations, slow, and dependent on input locations.